The AI Summit New York 2019
December 11, 2019, New York, NY, United States
The AI Summit New York 2019
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Experts in AI: Kirk Borne, principal data scientist, Booz Allen Hamilton
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About speakers

Kirk Borne
Principal Data Scientist and Executive Advisor, First Data Science Fellow at Booz Allen Hamilton
Amy Turner
Head of Digital Content at Informa Tech

Dr. Kirk Borne is the Principal Data Scientist and an Executive Advisor at global technology and consulting firm Booz Allen Hamilton. In those roles, he focuses on applications of data science, data management, machine learning, A.I., and modeling across a wide variety of disciplines. He also provides training and mentoring to executives and data scientists in the use of large data repositories and machine learning for discovery, decision support, and innovation. Previously, he was Professor of Astrophysics and Computational Science at George Mason University for 12 years where he did research, taught, and advised students in data science. Prior to that, Kirk spent nearly 20 years supporting data systems activities on NASA space science programs, which included a period as NASA's Data Archive Project Scientist for the Hubble Space Telescope, plus 10 years in management positions within NASA's Space Science Data Operations Office. Dr. Borne has a B.S. degree in Physics from LSU, and a Ph.D. in Astronomy from Caltech. In 2016 he was elected Fellow of the International Astrostatistics Association for his lifelong contributions to big data research in astronomy. As a global speaker, he has given hundreds of invited talks worldwide, including conference keynote presentations at many dozens of data science, A.I. and big data analytics events globally.

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Welcome to Addison's TV here at the anthem in New York 2019. We're very pleased to have with acid a chief data scientist from dear Evan Hansen. How you doing? Fantastic full of content brain busting. I love it. Thank you very much. I want to ask how did you first get into you take an AI in particular from data and develop the algorithm to Discovery from data, but that was all sort of static data. You're just sort of fixed numbers is about Dynamic data real-time data

streaming data making decisions in real time and enabling in power and machines to make decisions and take actions in real-time. That's pretty exciting. So I just was drawn into a sort of automatically by my interest in those that types of things dating algorithm and then I discovered Just about Discovery was about value creation and Innovation innovating new products creating new things. And then for the last big toe to push for me was that I started seeing the big societal impact we could have it to really address really serious problems with this technology in. I just couldn't

resist it at that point. Station ESO Booz, Allen Hamilton is a global technology and consulting firm or primary customer base is in the United States. And in fact primarily in the Washington DC area 70% of our business is our government clients 30% commercial and so in that space and space to specifically they are they all have lots of data other alternate automate processes and speed up process is drive faster Solutions in the whatever their missions have to be some of those missions are

health-related summer Social Security related summer Transportation summer energy since all over the place. We pretty much every Federal agency doing things to help them accelerated motivate the solutions and Innovation around their their processes and their data and so we have two teams of people who work in all those places cybersecurity is one of the biggest target areas and as well as health care of them and I always tell people the two biggest applications in the world of this stuff is cybersecurity because everyone is facing that problem and health care because we're all Enzo Booz

Allen is very involved with all those and bringing aai and Technology Solutions are not in that space. vet schools in TN government take that what you still think of the biggest misconception in terms of AI adoption industry describe. Someone is why we called sort of the general public misconception that AI is all about humanoid robots. All right and humans work in businesses. Sometimes the employees of businesses think it's about humanoid robots. And so they're going to lose their jobs. If you're going to give you this way. I didn't realize it's really about assisting and accelerating and

improving their work. And so that's what that's a big mistake that could actually within the Enterprise itself. A misconception is the sort of idea that that it's such everyone is doing a guy I better get on board with it and you sort of jump on board because of a fear of missing out. I mean in marketing we call that phone. All right so businesses get onto the iban weekend because they think they need to get on that iron bandwagon as opposed to say what what is our business? What are business goals? What are up? Actors in How can AI help us to reach our business goals? So so so stay true

to your North Star state route to why you are a business in the first place and then use this as a tool to help you do better and I was kind of organization for navigating that they are a map. There are lots of bumps in the Royal challengers in terms of getting this project off the ground, What time is it at used in an annual time to open a IRA if I want to buy something? If you give people too kind of, you know, stay clear of those on navigate the challenges that's really not an answer your question because it's culture so it does not have

its own after I answered your question cuz everyone will say yeah culture is your biggest problem. What does that mean? Okay. So for me more specifically that means you are one example of a culture problem is a data sharing a lot of organizations within the Enterprise. They don't share data like the sales department the customer Department then the procurement Department the HR department and they don't share data. SSI low busting a nut and data sharing is one of the biggest hurdles we have to overcome and it is at is a culture problem, but it's really about how to get access to the

data. You need to improve build these models train these models and uses my love. So that's one of the big challenge. The other big challenge is is being able to demonstrate the value of that. So I always tell people to think big butt start small his small implementations build a fantasy people in the organization. Once they see the results and successes from the small implementation that the vet that it's not about robots. It's not about me losing my job. It's about creating value for our business and I think everyone can get on board with that

I put in for a three categories, which one is about data. I think we're going to ReDiscover data management data curation. And the reason I say That is because a i is hungry starving for data unless we feed it good curated clean label tag data is just not going to be good. I mean that we want to buy a stock with them is what we'll have all kinds of issues with what we're building and so we can see A Renewed interest in and then clean data and data management data curation almost like a revisit to 20 years ago now realizing all that is

very important. So this there's one dimension other dimension is the human Dimension where I think organizations and businesses what we will wake up to the fact that they really need to reschedule an upscale employees around digital literacy. I would call that data literacy. So we're going to see a lot more sore and word sort of focus and it for a wild of a company say we have we got to bring our employees up to speed in this in and do some internal training classic. I think some of the data focus of the human Focus are two things and then there's an algorithm Focus. I'd like to say that

it's already talking about this year, but I see it more and more and that is this automatic if I machine learning tools Auto in How are you at? You start getting all these tool that can automatically got to click of a button classify an image of Earth are classified document putting those things in the hands of the scientists going to be happening more and more because you're so much data that we don't even need everyone in the organization to work with data. I always like to say others are just as a slogan in the New York City metro station system that says if you see something say

something and I think a lot of a lot of places in airports, you see that kind of sign it and that's the day that people who are working companies if they see something in the date of trend or pattern and interesting anomaly. They should say something it's not someone else's job. It's your job to okay, so we're going to empowering more and more people with these auto tools. Thank you so much for joining us. Enjoy the rest your ass on it.

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